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Stochastic flowering phenology in Dactylis Glomerata populations described by Markov chain modelling

Frisk, Carl A. ORCID: https://orcid.org/0000-0002-9722-2544, Adams-Groom, Beverley ORCID: https://orcid.org/0000-0002-1097-8876 and Skjøth, C. ORCID: https://orcid.org/0000-0001-5992-9568 (2020) Stochastic flowering phenology in Dactylis Glomerata populations described by Markov chain modelling. Aerobiologia. ISSN Print: 0393-5965 Online: 1573-3025 (In Press)

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Abstract

Understanding the relationship between flowering patterns and pollen dispersal is important in climate change modelling, pollen forecasting, forestry and agriculture. Enhanced understanding of this connection can be gained through detailed spatial and temporal flowering observations on a population level, combined with modelling simulating the dynamics. Species with large distribution ranges, long flowering seasons, high pollen production and naturally large populations can be used to illustrate these dynamics. Revealing and simulating species-specific demographic and stochastic elements in the flowering process will likely be important in determining when pollen release is likely to happen in flowering plants. Spatial and temporal dynamics of eight populations of Dactylis glomerata were collected over the course of two years to determine high-resolution demographic elements. Stochastic elements were accounted for using Markov Chain approaches in order to evaluate tiller-specific contribution to overall population dynamics. Tiller-specific developmental dynamics were evaluated using three different RV matrix correlation coefficients. We found that the demographic patterns in population development were the same for all populations with key phenological events differing only by a few days over the course of the seasons. Many tillers transitioned very quickly from non-flowering to full flowering, a process that can be replicated with Markov Chain modelling. Our novel approach demonstrates the identification and quantification of stochastic elements in the flowering process of D. glomerata, an element likely to be found in many flowering plants. The stochastic modelling approach can be used to develop detailed pollen release models for Dactylis, other grass species and probably other flowering plants.

Item Type: Article
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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Uncontrolled Discrete Keywords: grass ecology, grass pollen, grass, allergen, stochastic, flowering dynamics, SERG, pollen statistics, anthesis, poaceae, UK, pollen
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > Q Science (General)
Q Science > QK Botany
S Agriculture > S Agriculture (General)
Divisions: College of Health, Life and Environmental Sciences > School of Science and the Environment
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Depositing User: Carl Frisk
Date Deposited: 17 Dec 2020 09:03
Last Modified: 13 May 2021 12:39
URI: https://eprints.worc.ac.uk/id/eprint/10062

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